network inference umer zeeshan ijaz 1. overview introduction application areas cdna microarray...

Network Inference

Umer Zeeshan Ijaz

1

Overview

•Introduction•Application Areas

• cDNA Microarray• EEG/ECoG

•Network Inference• Pair-wise Similarity Measures

• Cross-correlation STATIC• Coherence STATIC

• Autoregressive• Granger Causality STATIC

• Probabilistic Graphical Models• Directed

• Kalman-filtering based EM algorithm STATIC • Undirected

• Kernel-weighted logistic regression method DYNAMIC• Graphical Lasso-model STATIC

Introduction

cDNA Microarray

EoCG/EEG

For a pair of time series xi[t] and xj[t] of lengths n, the sample correlation at lag τ

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Use scaled value that is expected to behave like the maximum of the absolute value of a sequence of random numbers. Using now established results for statistics of this form, we obtain therefore that

*M. A. Kramer, U. T. Eden, S. S. Cash, E. D. Kolaczyk, Network inference with confidence from multivariate time series. Physical review E 79, 061916, 2009

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1) Compute the power spectrum (Hanning tapered) of each series and average these power spectra from all the time series

2) Compute the standardized and whitened residuals for each time series

3) For each bootstrap replicate, RESAMPLE WITH REPLACEMENT and compute the surrogate data

4) Compute such instances and calculate maximum cross-correlation for each pair of nodes i and j

5) Finally compare the bootstrap distribution and assign a p-value

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False Detection Rate Test

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*M. A. Kramer, U. T. Eden, S. S. Cash, and E. D. Kolaczyk. Network inference with confidence from multivariate time series, Physics Review E 79(061916), 1-13, 2009

Cross-correlation based (4)

Coherence: Signals are fully correlated with constant phase shifts, although they may show difference in amplitude

Cross-phase spectrum: Provides information on time-relationships between two signals as a function of frequency. Phase displacement may be converted into time displacement

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Coherence based(2)

*S. Weiss, and H. M. Mueller. The contribution of EEG coherence to the investigation of language, Brain and Language 85(2), 325-343, 2003

Directed Transfer Function: Directional influences between any given pair of channels in a multivariate data set

Bivariate autoregressive process

If the variance of the prediction error is reduced by the inclusion of other series, then based on granger causality, one depends on another. Now taking the fourier transform

Granger causality from channel j to i:

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- State Space Model (State Variable Model; State Evolution Model)

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Kalman Filter

Probabilistic graphical models(1)

nX,,X=X ...1Joint distribution over a set

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Kalman filter based: Inferring network from microarray expression data(5)

Experimental Results: A standard T-Cell activation model

*Claudia Rangel, John Angus, Zoubin Ghahramani, Maria Lioumi, Elizabeth Sotheran, Alessia Gaiba, David L. Wild, Francesco Falciani: Modeling T-cell activation using gene expression profiling and state-space models. Bioinformatics 20(9): 1361-1372 (2004)

Kalman filter based: Inferring network from microarray expression data(9)

Probabilistic graphical models(2)

Markov Networks represent joint distribution as a product of potentials

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Interaction between gene ontological groups related to developmental process undergoing dynamic rewiring. The weight of an edge between two ontological groups is the total number of connection between genes in the two groups. In the visualization, the width of an edge is propotional to the edge weight. The edge weight is thresholded at 30 so that only those interactions exceeding this number are displayed. The average network on left is produced by averaging the right side. In this case, the threshold is set to 20

*L. Song, M. Kolar, and E. P. Xing. KELLER: estimating time-varying interactions between genes. Bioinformatics 25, i128-i136, 2009

Graphical Lasso Model(1)

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*O. Banerjee, L. E. Ghaoui, A. d’Aspremont. Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. Journal of Machine Language Research 101, 2007

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*O. Banerjee, L. E. Ghaoui, A. d’Aspremont. Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. Journal of Machine Language Research 101, 2007

Graphical Lasso Model(3)

*Software under development @ Oxford Complex Systems Group with Nick Jones*Results shown for Google Trend Dataset

27

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